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521
Application of Multi-Scale Geological Modeling Technology in Sweet Spot Prediction of Shale Oil Reservoirs
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522
Evaluation of Statistical Models of NDVI and Agronomic Variables in a Protected Agriculture System
Published 2025-01-01“…This has created a database to generate predictive models of development and yield as a function of nutrient status. …”
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523
Evolution and Predictive Analysis of Spatiotemporal Patterns of Habitat Quality in the Turpan–Hami Basin
Published 2024-12-01“…Additionally, the InVEST-PLUS coupling model was employed to forecast habitat conditions under three different scenarios in 2050. …”
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524
Quantifying 3D coral reef structural complexity from 2D drone imagery using artificial intelligence
Published 2025-03-01“…The validation of our model resulted in R2 values of 0.71, 0.65, and 0.56 for each metric, respectively, indicating a robust predictive capability. …”
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525
Establishment of agricultural drought monitoring at different spatial scales in southeastern Europe
Published 2010-10-01“…In the study two specific products designed for regional scale are described: preliminary maps of the SPI (Standardized Precipitation Index) and products generated by implementation of numerical weather prediction model. It seems to be a lot of potential in both products for a first overview of key meteorological parameters in the region. …”
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526
Current and future climate suitability for the hazel dormouse in the UK and the impact on reintroduced populations
Published 2024-12-01“…Here, we use species distribution models (SDMs) to map climate suitability for dormice in the UK. …”
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527
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Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model
Published 2024-06-01“…To improve the prediction accuracy of multi-region power load, an ultra-short-term multi-region power load forecasting model based on Spearman-GCN-GRU is proposed with focus on the spatial-temporal correlation analysis of multi-region power data. …”
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530
A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024)
Published 2025-07-01“…The findings indicate that Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) are the most frequently used algorithms, with prediction accuracy ranging from 72% to 99.92%. Dominant predictor variables include maternal education, economic status, sanitation, and spatial-temporal data. …”
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531
Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.
Published 2021-01-01“…However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. …”
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532
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Analyzing the determinant factors of spatial groundwater availability in the Akaki catchment, Central Ethiopia
Published 2025-12-01“…Hence, this study investigated the spatial availability of groundwater within the catchment by considering eight different factors. …”
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535
Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china
Published 2025-02-01“…By using multi-source big data, this paper analyzes the population distribution, traffic organization, infrastructure, land use and regional economy of Jinan urban area, China, and constructs a comprehensive evaluation index system to predict the spatial demand of PCS for EVs. We analyse: (1) Distribution of population activities on weekday and rest days, the closeness and betweenness of road network, high-density area, commerce, public service facilities, parks, transportation facilities, residential area, building coverage, floor area ratio, economic development area and housing price level. (2) Correlation and influence weights of 14 evaluation indexes and PCS layout. (3) Prediction of spatial demand distribution of PCS. (4) Comparison of current PCS distribution and spatial demand prediction results. …”
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536
Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study
Published 2024-12-01“…This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.Methods Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. …”
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537
The spatial risk of cyclone wave damage across the Great Barrier Reef
Published 2025-11-01“…We then applied a statistical model with likelihood inference to predict damage given cyclone strength and reef spatial arrangement, and calibrated the model using field observations from five cyclones. …”
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538
Graph neural network driven traffic prediction technology:review and challenge
Published 2021-12-01“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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539
Graph neural network driven traffic prediction technology:review and challenge
Published 2021-12-01“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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540
